Medical image classification using an efficient data mining technique

Islam, Md. Rafiqul, Chowdhury, Morshed and Khan, Safwan 2004, Medical image classification using an efficient data mining technique, in Complex 2004 : Proceedings of the 7th Asia-Pacific Complex Systems Conference, Central Queensland University, Rockhampton, Qld., pp. 34-42.

Attached Files
Name Description MIMEType Size Downloads

Title Medical image classification using an efficient data mining technique
Author(s) Islam, Md. Rafiqul
Chowdhury, Morshed
Khan, Safwan
Conference name Asia-Pacific Complex Systems Conference (7th : 2004 : Cairns, Qld.)
Conference location Cairns, Qld.
Conference dates 6-10 December 2004
Title of proceedings Complex 2004 : Proceedings of the 7th Asia-Pacific Complex Systems Conference
Editor(s) Stonier, Russel
Han, Qinglong
Li, Wei
Publication date 2004
Start page 34
End page 42
Publisher Central Queensland University
Place of publication Rockhampton, Qld.
Keyword(s) data mining
classification
medical imaging
decision tree
feature selection
Summary Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.
ISBN 1876674962
9781876674960
Language eng
Field of Research 080106 Image Processing
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005389

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 878 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 09:49:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.